Planning, Implementing, and Assessing an OER Faculty Learning Community: A Facilitator’s Lens
Bibliographic record
Abstract
A librarian-led Faculty Learning Community (FLC) focused on Open Educational Resources (OER) can be a practical, low risk way to sustain campus-based OER programs during and after initial start-up. Creating a space for sharing teaching successes and challenges is an important goal in the iterative journey toward open. The experiences and trust fostered in an FLC can help grow awareness of and commitment to adopting, deepening, and expanding a culture of openness. FLCs provide an opportunity to lean into open that enhances cross-campus relationships, identifies gaps, and emphasizes collegiality while moving toward enriched teaching and learning. They provide a launching point for sharing pedagogical practice, and a valuable venue for new ideas. Key strategies for planning, implementing, and assessing a multidisciplinary OER faculty learning community are highlighted. Practical advice is emphasized to support successful outcomes that can be easily replicated. Ten top takeaways are summarized from a year spent facilitating an OER FLC in a four-year, public, comprehensive college that included the shift to online courses during the COVID-19 pandemic, and it concludes with suggested next steps for continuing the OER conversation among faculty, students, librarians, instructional designers, teaching and learning center staff, administration, and other stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".